{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cab6db1f",
   "metadata": {},
   "source": [
    "## 预备知识"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82f8d2ab",
   "metadata": {},
   "source": [
    "安装Python以及Numpy、SciPy、Matplotlib、Scikit-learn。\n",
    "## Numpy\n",
    "NumPy是Python的基础数值计算库，提供高效的**多维数组对象**（ndarray）和数组操作工具。\n",
    "- 高性能数组计算：基于连续内存存储和C语言底层优化，支持向量化运算，比原生Python列表快数十倍。\n",
    "- 数学函数库：涵盖线性代数、傅里叶变换、随机数生成等。\n",
    "- 广播机制：支持不同形状数组的算术运算。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0aebc37f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.6\n",
      "[1 2 3 4]\n",
      "4\n",
      "(4,)\n",
      "int64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "print(np.__version__)\n",
    "a = np.array([1, 2, 3, 4])\n",
    "print(a)\n",
    "print(a.size)\n",
    "print(a.shape)\n",
    "print(a.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa795bd3",
   "metadata": {},
   "source": [
    "Numpy以数组为运算对象，可以方便地将列表转换为数组，且Numpy的数组具有很高的运算效率。\n",
    "- size属性表示元素个数。\n",
    "- shape属性是一个元组，表示数组形状。\n",
    "- dtype属性表示数组中元素的数据类型（与C语言的数值类型相对应，如float64, float32, int64, uint64, int32, uint32, uint8等）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "470fe913",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint8\n",
      "float64\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python integer 333 out of bounds for uint8",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m c \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m4.0\u001b[39m])\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(c\u001b[38;5;241m.\u001b[39mdtype)\n\u001b[1;32m----> 5\u001b[0m d \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m111\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m222\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m333\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m444\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muint8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28mprint\u001b[39m(d)\n",
      "\u001b[1;31mOverflowError\u001b[0m: Python integer 333 out of bounds for uint8"
     ]
    }
   ],
   "source": [
    "b = np.array([1, 2, 3, 4], dtype=\"uint8\")\n",
    "print(b.dtype)\n",
    "c = np.array([1, 2, 3, 4.0])\n",
    "print(c.dtype)\n",
    "d = np.array([111, 222, 333, 444], dtype=\"uint8\")\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8ebf071",
   "metadata": {},
   "source": [
    "把列表转换为数组得到一维向量，把包含列表的列表转换为数组，就得到了二维向量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "95bef657",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 3)\n",
      "9\n",
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
    "print(x.shape)\n",
    "print(x.size)\n",
    "print(x)\n",
    "print(x[1, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "158dd046",
   "metadata": {},
   "source": [
    "Numpy方法np.zeros和np.ones可以获得指定形状（shape）的全零或全一数组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cf4e6cf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]]\n",
      "uint32\n",
      "[1. 1. 1.]\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "y = np.zeros((3, 4), dtype=\"uint32\")\n",
    "print(y)\n",
    "print(y.dtype)\n",
    "z = np.ones(3)\n",
    "print(z)\n",
    "print(z.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ddf541d",
   "metadata": {},
   "source": [
    "Numpy支持复杂的索引形式，比如用单个索引查询整个子数组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "adb17605",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n",
      "[4 5 6 7]\n",
      "[[ 0  1  2  3]\n",
      " [44 55 66 77]\n",
      " [ 8  9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "n = np.arange(12).reshape(3, 4)\n",
    "print(n)\n",
    "print(n[1])\n",
    "n[1] = [44, 55, 66, 77]\n",
    "print(n)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cd13512",
   "metadata": {},
   "source": [
    "Numpy支持Python列表的所有切片索引方式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3930de58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [44 55 66 77]]\n"
     ]
    }
   ],
   "source": [
    "print(n[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e4e7c169",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [44 55 66 77]]\n"
     ]
    }
   ],
   "source": [
    "print(n[:2, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fb548c35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2]\n",
      " [44 55 66]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(n[:2, :3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "766eb0e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[44 55 66 77]\n",
      " [ 8  9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(n[1:, ...])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fd725be5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11]\n",
      "[ 0  2  4  6  8 10]\n",
      "[11 10  9  8  7  6  5  4  3  2  1  0]\n"
     ]
    }
   ],
   "source": [
    "m = np.arange(12)\n",
    "print(m)\n",
    "print(m[::2])\n",
    "print(m[::-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a95c0b6",
   "metadata": {},
   "source": [
    "Numpy可以将数组通过np.save保存到磁盘上，还可以通过np.load从磁盘上加载。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5c7f281d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 4 1 1]\n",
      " [4 1 4 2]\n",
      " [3 3 2 2]]\n"
     ]
    }
   ],
   "source": [
    "s = np.random.randint(0, 5, (3, 4))\n",
    "print(s)\n",
    "np.save(\"random.npy\", s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40db1b16",
   "metadata": {},
   "source": [
    "np.random.randint创建了一个形状为$3\\times4$，元素取值在0到5之间（包含0和5）的随机整型数组（Numpy中有很多关于随机数的函数）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "429c55ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 4 1 1]\n",
      " [4 1 4 2]\n",
      " [3 3 2 2]]\n"
     ]
    }
   ],
   "source": [
    "ss = np.load(\"random.npy\")\n",
    "print(ss)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb168099",
   "metadata": {},
   "source": [
    "## SciPy\n",
    "SciPy基于NumPy，提供更高级的科学计算工具。\n",
    "- 数值积分（scipy.integrate）、优化算法（scipy.optimize）、信号处理（scipy.signal）。\n",
    "- 稀疏矩阵处理（scipy.sparse）、统计检验（scipy.stats）。\n",
    "- 插值与空间分析（如 Dijkstra 最短路径算法）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "df03594e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TtestResult(statistic=np.float64(0.7160608160671735), pvalue=np.float64(0.47403747653154193), df=np.float64(1998.0))\n",
      "TtestResult(statistic=np.float64(-0.809461119457486), pvalue=np.float64(0.4183463437438377), df=np.float64(1998.0))\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import ttest_ind\n",
    "a = np.random.normal(0, 1, 1000)\n",
    "b = np.random.normal(0, 0.5, 1000)\n",
    "c = np.random.normal(0.1, 1, 1000)\n",
    "r1 = ttest_ind(a, b)\n",
    "print(r1)\n",
    "r2 = ttest_ind(a, c)\n",
    "print(r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c856c1a",
   "metadata": {},
   "source": [
    "ttest_ind函数用于执行独立样本t检验（Independent Samples t-test），这是一种统计检验方法，用于比较两个独立样本组的均值是否有显著差异。主要作用包括：\n",
    "- 检验两个独立样本组的均值是否存在统计学上的显著差异。\n",
    "- 计算t统计量和对应的p值。\n",
    "- 默认假设两组数据具有相同的方差（可以通过参数调整）。\n",
    "\n",
    "返回值中，statistic是t统计量，pvalue是对应的p值，df是自由度。如果pvalue小于显著性水平（通常为0.05），则拒绝原假设，认为两组均值存在显著差异。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea365e2a",
   "metadata": {},
   "source": [
    "## Matplotlib\n",
    "\n",
    "Matplotlib是Python的主流绘图库，支持：\n",
    "- 多种图表类型：折线图、散点图、直方图、3D图等。\n",
    "- 高度定制化：可调整颜色、标签、图例等细节，适合学术出版。\n",
    "- 交互式界面：与Jupyter Notebook集成，支持动态可视化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6f422122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "x = np.random.random(100)\n",
    "plt.plot(x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "87ae0650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "x = np.random.random(20)\n",
    "y = np.random.random(20)\n",
    "z = np.random.random(20)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(x, y, z)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "833afd8c",
   "metadata": {},
   "source": [
    "1. 加载三维坐标组件、matplotlib和numpy\n",
    "2. 用numpy生成了3各取值区间为$[0, 1)$的随机数组，作为绘图的数据点\n",
    "3. 用plt.figure和fig.add_subplot设置三维投影图，参数：\n",
    "   - 111，整个图片由1✖1的网格构成，并把当前图片放到第一个格子里，所以111表示只有单张图片。\n",
    "   - projection，设置三维绘图模式。\n",
    "4. ax.scatter绘制散点图，并通过plt.show显示绘制后的图片。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef1fcc15",
   "metadata": {},
   "source": [
    "## Scikit-learn\n",
    "\n",
    "Scikit-learn是Python的机器学习核心库。\n",
    "- 统一 API 设计：所有模型均通过fit()、predict()方法调用，简化使用流程。\n",
    "- 丰富算法：覆盖分类（SVM、随机森林）、回归、聚类（K-Means）、降维（PCA）等。\n",
    "- 预处理与评估：支持数据标准化、交叉验证、网格搜索调参等。\n",
    "\n",
    "本课程会用到SKlearn中的MLPClassifier类，另外它还提供了很多用于评估模型性能和绘制高维图像的组件。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04a193ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score:  0.97\n",
      "errors:\n",
      "actual:  [8 3 9 8 9 1]\n",
      "predicted:  [5 8 8 6 5 8]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "d = load_digits()  # d为一个包含了1797个8×8（图片）的数据集。\n",
    "digits = d[\"data\"]  # (1797, 64)\n",
    "labels = d[\"target\"]  # (1797,)\n",
    "\n",
    "N = 200\n",
    "idx = np.argsort(np.random.random(len(labels)))\n",
    "x_test, y_test = digits[idx[:N]], labels[idx[:N]]  # 取前200个数据作为测试集，剩余部分为训练集。\n",
    "x_train, y_train = digits[idx[N:]], labels[idx[N:]]\n",
    "\n",
    "clf = MLPClassifier(hidden_layer_sizes=(128, ))  # 构建单个隐藏层为128个神经元的多层感知机网络。\n",
    "clf.fit(x_train, y_train)  # 传入训练数据及其对应的标签，进行训练，训练后的模型会保存在clf中。\n",
    "\n",
    "score = clf.score(x_test, y_test)  # 通过测试集评估模型的效果（准确率）。\n",
    "pred = clf.predict(x_test)  # 通过clf中的模型对测试集数据进行推理。\n",
    "err = np.where(y_test != pred)[0]  # 找出推理结果不符合正确值的索引。\n",
    "print(\"score: \", score)\n",
    "print(\"errors:\")\n",
    "print(\"actual: \", y_test[err])\n",
    "print(\"predicted: \", pred[err])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6fc247b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2]\n",
      "[11 33]\n",
      "[10 30]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([11, 22, 33, 44])\n",
    "b = np.array([10, 22, 30, 44])\n",
    "diff = np.where(a != b)[0]\n",
    "print(diff)\n",
    "print(a[diff])\n",
    "print(b[diff])"
   ]
  }
 ],
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